Research on Modeling and Optimization of Missile Smoke Screen Jammer System Based on Genetic Algorithm and Greedy Algorithm

Abstract

This paper proposes modeling and solution methods for aerial vehicle jamming systems, focusing on jamming cloud motion patterns, aerial vehicle occlusion detection, and multi-jamming cloud collaborative optimization. First, a jamming cloud motion model is established across three time intervals. The vertical foot method determines the closest point between the jamming cloud center and the line connecting the aerial vehicle and target. Combined with the discrete time point method, time discretization, distance calculation, and decision functions are employed to construct the occlusion duration solution. Second, addressing the parameter optimization problem, a genetic algorithm is employed. Chromosomes containing optimization variables are constructed, with the objective function serving as the fitness function, to solve for optimized parameters and corresponding occlusion durations. Finally, the model is extended to multi-jamming-cloud scenarios. Based on the minimum distance criterion, it determines whether the flight vehicle is obscured. Through inner-outer network searches, parameter separation, variable dimension reduction, and combined with a greedy algorithm, it iteratively optimizes the deployment and detonation times of jamming munitions, targeting additional obstruction duration. This model accurately depicts the interaction process between jamming clouds and flight vehicles. The algorithms employed adapt to both single- and multi-jamming-cloud scenarios, effectively enhancing jamming effectiveness and parameter optimization efficiency.